cikm 2015 论文列表

Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2015, Melbourne, Australia, October 23, 2015.

Prediction of Scaffold Proteins based on Protein Interaction and Domain Architectures.
Cell line specific method based on functional modules for drug sensitivity prediction.
What-if Analysis in Biomedical Networks based on Production Rule System.
Analyzing Subject-Method Network of Bioinformatics and Biology.
A Flexible Text Mining System for Entity and Relation Extraction in PubMed.
A Text Mining Approach for Identifying Herb-chemical Relationships from Biomedical Articles.
Analyzing the Landscape of Anti-Cancer Drug Research in Pancreatic Cancer.
Citizen Organization System for Advanced MEDical research (COSAMED).
Identification and Analysis of Medical Entity Co-occurrences in Twitter.
Toxicity Predictions using Compound Descriptions.
Evaluation of Disease-Associated Text-Mining Databases.
Identifying Potential Bioactive Compounds of Natural Products by Combining ADMET Prediction Methods.
Development of a Framework for Constructing a Virtual Physiological Human with the Integration of COntext Specific Directed Associations (CODA).
Building Text-mining Framework for Gene-Phenotype Relation Extraction using Deep Leaning.
Prognostic Factor Analysis for Breast Cancer Using Gene Expression Profiles.
Prediction of Compound-Target Interactions of Natural Products Using Large-scale Drug and Protein Information.
In-silico Interaction-Resolution Pathway Activity Quantification and Application to Identifying Cancer Subtypes.
Discovery of Prostate Specific Antigen Pattern to Predict Castration Resistant Prostate Cancer of Androgen Deprivation Therapy.
Evaluation of a Machine Learning Duplicate Detection Method for Bioinformatics Databases.
Extraction of Fine-grained Semantic Relations for the Human Variome.
Privacy Preserving Data Anonymization of Spontaneous ADE Reporting System Dataset.
The Effect of Word Sense Disambiguation Accuracy on Literature Based Discovery.